E-Book, Englisch, 396 Seiten
ISBN: 978-0-12-803492-7
Verlag: Elsevier Textbooks
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Includes hundreds of updated and new, real-world examples to engage students in the meaning and impact of statisticsFocuses on essential information to enable students to develop their own statistical reasoningIdeal for one-quarter or one-semester courses taught in economics, business, finance, politics, sociology, and psychology departments, as well as in law and medical schools
Gary Smith received his B.S. in Mathematics from Harvey Mudd College and his PhD in Economics from Yale University. He was an Assistant Professor of Economics at Yale University for seven years. He is currently the Fletcher Jones Professor of Economics at Pomona College. He has won two teaching awards and has written (or co-authored) seventy-five academic papers, eight college textbooks, and two trade books (most recently, Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie With Statistics, Overlook/Duckworth, 2014). His research has been featured in various media including the New York Times, Wall Street Journal, Motley Fool, NewsWeek and BusinessWeek. For more information visit www.garysmithn.com.
Autoren/Hrsg.
Weitere Infos & Material
1 Data, Data, Data
Abstract
Data and statistical analysis can help us understand the world and predict the consequences of events. Numerical data have natural numerical values, like 5.1 percent unemployment; categorical data count the number of observations in each category, like 47 females and 34 males. Some data are cross-sectional observations made at the same point in time, like the price/earnings ratios for each of the stocks in the Dow Jones Industrial Average on February 5, 2015. Other data are time series observations at different points of time, like the monthly unemployment rate since 1990. Longitudinal data (or panel data) involve repeated observations of the same things at different points in time, like the prices of 30 stocks every day for the past 12 months. Per capita data are adjusted for the size of the population. Real data are adjusted for prices. Keywords
Categorical data; Cross-sectional; Longitudinal (panel); Nominal; Numerical data; Per capita; Real; Time series You're right, we did it. We're very sorry. But thanks to you, we won't do it again. Ben Bernanke Chapter Outline Measurements 2 Flying Blind and Clueless 3 Testing Models 4 The Political Business Cycle 5 Making Predictions 5 Okun's Law 5 Numerical and Categorical Data 6 Cross-Sectional Data 6 The Hamburger Standard 7 Time Series Data 8 Silencing Buzz Saws 8 Longitudinal (or Panel) Data 10 Index Numbers (Optional) 10 The Consumer Price Index 11 The Dow Jones Index 12 Deflated Data 12 Nominal and Real Magnitudes 13 The Real Cost of Mailing a Letter 15 Real Per Capita 16 Exercises 16 The Great Depression was a global economic crisis that lasted from 1929 to 1939. Millions of people lost their jobs, their homes, and their life savings. Yet, government officials knew too little about the extent of the suffering, because they had no data measuring output or unemployment. They instead had anecdotes: “It is a recession when your neighbor is unemployed; it is a depression when you lose your job.” Herbert Hoover was president of the United States when the Great Depression began. He was very smart and well-intentioned, but he did not know that he was presiding over an economic meltdown because his information came from his equally clueless advisors—none of whom had yet lost their jobs. He had virtually no economic data and no models that predicted the future direction of the economy. In his December 3, 1929, State of the Union message, Hoover concluded that “The problems with which we are confronted are the problems of growth and progress” [1]. In March 1930, he predicted that business would be normal by May [2]. In early May, Hoover declared that “we have now passed the worst” [3]. In June, he told a group that had come to Washington to urge action, “Gentlemen, you have come 60 days too late. The depression is over” [4]. A private organization, the National Bureau of Economic Research (NBER), began estimating the nation's output in the 1930s. There were no regular monthly unemployment data until 1940. Before then, the only unemployment data were collected in the census, once every 10 years. With hindsight, it is now estimated that between 1929 and 1933, national output fell by one-third, and the unemployment rate rose from 3 percent to 25 percent. The unemployment rate averaged 19 percent during the 1930s and never fell below 14 percent. More than a third of the nation's banks failed and household wealth dropped by 30 percent. Behind these aggregate numbers were millions of private tragedies. One hundred thousand businesses failed and 12 million people lost their jobs, income, and self-respect. Many lost their life savings in the stock market crash and the tidal wave of bank failures. Without income or savings, people could not buy food, clothing, or proper medical care. Those who could not pay their rent lost their shelter; those who could not make mortgage payments lost their homes. Farm income fell by two-thirds and many farms were lost to foreclosure. Desperate people moved into shanty settlements (called Hoovervilles), slept under newspapers (Hoover blankets), and scavenged for food where they could. Edmund Wilson [5] reported that: There is not a garbage-dump in Chicago which is not haunted by the hungry. Last summer in the hot weather when the smell was sickening and the flies were thick, there were a hundred people a day coming to one of the dumps. Measurements
Today, we have a vast array of statistical data that can help individuals, businesses, and governments make informed decisions. Statistics can help us decide which foods are healthy, which careers are lucrative, and which investments are risky. Businesses use statistics to monitor operations, estimate demand, and design marketing strategies. Government statisticians measure corn production, air pollution, unemployment, and inflation. The problem today is not a scarcity of data, but rather the sensible interpretation and use of data. This is why statistics courses are taught in high schools, colleges, business schools, law schools, medical schools, and Ph.D. programs. Used correctly, statistical reasoning can help us distinguish between informative data and useless noise, and help us make informed decisions. Flying Blind and Clueless
US government officials had so little understanding of economics during the Great Depression that even when they finally realized the seriousness of the problem, their policies were often counterproductive. In 1930, Congress raised taxes on imports to record levels. Other countries retaliated by raising their taxes on goods imported from the United States. Worldwide trade collapsed with US exports and imports falling by more than 50 percent. In 1931, Treasury Secretary Andrew Mellon advised Hoover to “liquidate labor, liquidate stocks, liquidate the farmers, liquidate real estate” [6]. When Franklin Roosevelt campaigned for president in 1932, he called Hoover's federal budget “the most reckless and extravagant that I have been able to discover in the statistical record of any peacetime government anywhere, anytime” [7]. Roosevelt promised to balance the budget by reducing government spending by 25 percent. One of the most respected financial leaders, Bernard Baruch, advised Roosevelt to “Stop spending money we haven't got. Sacrifice for frugality and revenue. Cut government spending—cut it as rations are cut in a siege. Tax–tax everybody for everything” [8]. Today—because we have models and data—we know that cutting spending and raising taxes are exactly the wrong policies for fighting an economic recession. The Great Depression did not end until World War II, when there was a massive increase in government spending and millions of people enlisted in the military. The Federal Reserve (the “Fed”) is the government agency in charge of monetary policy in the United States. During the Great Depression, a seemingly clueless Federal Reserve allowed the money supply to fall by a third. In their monumental work, A Monetary History of the United States, Milton Friedman and Anna Schwartz argued that the Great Depression was largely due to monetary forces, and they sharply criticized the Fed's perverse policies. In a 2002 speech honoring Milton Friedman's 90th birthday, Ben Bernanke, who became Fed chairman in 2006, concluded his speech: “I would like to say to Milton and Anna: Regarding the Great Depression. You're right, we did it. We're very sorry. But thanks to you, we won't do it again” [9]. During the economic crisis that began in the United States in 2007, the president, Congress, and Federal Reserve did not repeat the errors of the 1930s. Faced with a credit crisis that threatened to pull the economy into a second Great Depression, the government did the right thing by pumping billions of dollars into a deflating economy. Instead of destroying the economy, they saved it. Why do we now know that cutting spending, raising taxes, and reducing the money supply are the wrong policies during economic recessions? Because we now have reasonable economic models that have been tested with data. Testing Models
The great British economist John Maynard Keynes observed that the master economist “must understand symbols and speak in words” [10]. We need words to explain our reasoning, but we also need models so that our theories can be tested with data. In the 1930s, Keynes hypothesized that household spending rises and falls with income. This “consumption function” was the lynchpin of his explanation of business cycles. If people spend less, others will earn less and spend less, too. This fundamental interrelationship between spending and income explains how recessions can persist and grow like a snowball rolling downhill. If, on the other hand, people buy more coal from a depressed coal-mining area, the owners and miners will then buy more and better...
